<p><strong>In a nutshell</strong></p> <p>Leads the engineering and deployment of production-ready Machine Learning systems that bring Data Science models to life at scale for Sainsbury's customers. Owns the technical implementation of ML pipelines, infrastructure, and operational excellence, ensuring models perform reliably in production. Shows care for both the customer experience and Data Science colleagues by building robust, maintainable solutions and refusing to walk past technical debt or operational issues.</p> <p>&nbsp;</p> <p><strong>What I am accountable for</strong></p> <ul> <li>Design and implement production-grade ML pipelines and infrastructure that enable reliable, scalable deployment of machine learning models across Sainsbury's products and services.</li> <li>Lead the engineering delivery of complex ML systems, making technical decisions on tooling, frameworks, and implementation approaches that balance performance, maintainability, and development velocity.</li> <li>Collaborate closely with Data Scientists and Data Analysts to operationalize models, translating experimental code into production-ready solutions while maintaining model accuracy and establishing robust testing frameworks.</li> <li>Establish and maintain MLOps practices including CI/CD pipelines, model versioning, monitoring, alerting, and automated retraining workflows to ensure continuous model performance and reliability.</li> <li>Optimise ML system performance through efficient resource utilization, cost management, and performance tuning of inference pipelines, ensuring solutions meet latency and throughput requirements.</li> <li>Provide technical leadership and mentorship to ML Engineers, sharing best practices, conducting code reviews, and building team capability in ML engineering principles and production systems.</li> <li>Identify and resolve technical debt, performance bottlenecks, and operational issues in ML systems, implementing preventative measures and contributing to architectural improvements.</li> <li>Support incident response for production ML systems, diagnosing and resolving model degradation, pipeline failures, and infrastructure issues while implementing improvements to prevent recurrence.</li> </ul> <p>&nbsp;</p> <p><strong>What I need to know</strong></p> <p>&nbsp;</p> <p><strong>Essential</strong></p> <ul> <li>Expert-level proficiency in Python and ML frameworks with deep understanding of model deployment and optimization</li> <li>Advanced experience with cloud platforms (AWS and Azure) including ML services (Azure ML) and container orchestration (Docker, Kubernetes, EKS/AKS) along with infrastructure as code (Terraform)</li> <li>Strong expertise in MLOps tooling and practices including CI/CD pipelines (GitHub Actions), model versioning (MLflow), and experiment tracking</li> <li>Proficient in building and optimising ML pipelines using workflow orchestration tools (Airflow)</li> <li>Strong understanding of software engineering best practices including version control (Git), testing frameworks, code review, and documentation</li> <li>Experience in Monitoring and observability for ML systems including model performance tracking, data drift detection, and system health metrics</li> <li>Experience in Data engineering fundamentals including data pipelines, data quality, and integration with data platforms (DBT, Kafka)</li> <li>Experience in Security and governance practices for ML systems including model security, data privacy, and compliance requirements</li> <li>Extensive experience building and deploying ML systems in production environments at scale</li> <li>Demonstrable experience providing technical leadership and mentoring to engineers</li> <li> <p>Strong background in debugging complex distributed systems and resolving production incidents</p> <p>&nbsp;</p></li> </ul> <p><strong>Desirable</strong></p> <ul> <li>Experience with feature stores and model registries for ML metadata management</li> <li>Knowledge of advanced ML deployment patterns including A/B testing, canary deployments.</li> <li>Experience with streaming ML architectures and real-time inference pipelines</li> <li>Understanding of distributed training frameworks and GPU optimization</li> <li>Knowledge of AutoML platforms and tools</li> <li>Awareness of emerging ML engineering tools, frameworks, and industry best practices</li> <li>Experience establishing ML engineering standards and best practices across teams</li> </ul> <p>&nbsp;</p> <p><strong>What I need to show</strong></p> <p>&nbsp;</p> <p><strong>Own it</strong></p> <p>&nbsp;</p> <ul> <li>Take full ownership of ML systems in production, monitoring performance proactively and responding swiftly to incidents, ensuring models deliver consistent value to customers without passing problems to others</li> <li>Deliver on engineering commitments to Data Science teams and stakeholders, meeting agreed timelines for model deployment and maintaining transparent communication when challenges arise</li> <li>Hold yourself accountable for code quality and system reliability, implementing comprehensive testing, monitoring, and documentation even when under delivery pressure</li> <li>Don't walk past technical debt or operational issues, raising concerns early and proposing solutions that prevent small problems from becoming major incidents</li> </ul> <p>&nbsp;</p> <p><strong>Make it better</strong></p> <p>&nbsp;</p> <ul> <li>Continuously optimise ML systems for performance, cost, and maintainability, identifying opportunities to reduce inference latency, lower cloud costs, or simplify pipeline complexity</li> <li>Improve the experience for Data Scientists by contributing towards reusable tools, frameworks, and automation that accelerate the path from model development to production deployment</li> <li>Spot opportunities to enhance customer outcomes through better model performance, faster feature delivery, or improved system reliability, using data to measure and demonstrate impact</li> <li> <p>Challenge inefficient processes and practices, proposing engineering improvements that simplify workflows while maintaining quality and security standards</p> <p>&nbsp;</p></li> </ul> <p><strong>Be human</strong></p> <p>&nbsp;</p> <ul> <li>Collaborate empathetically with Data Scientists, understanding their experimental workflows and translating research code into production systems while respecting their expertise and contributions</li> <li>Mentor ML Engineers with patience and encouragement, sharing knowledge generously, providing constructive code review feedback, and investing time to develop their technical capabilities</li> <li>Communicate technical concepts clearly to diverse audiences including non-technical stakeholders, adapting your approach to ensure understanding and building trust through transparency</li> <li>Show respect for your colleagues' time and expertise by writing clear documentation, conducting thorough handovers, and being responsive when others need your support or guidance</li> </ul>